8 papers · 1 filter
Neural Integral Operators for Inverse Problems: An Operator-Learning Framework for Small-Sample Spectroscopic Classification
Emanuele Zappala, Alice Giola, Andreas Kramer +2
Learning maps between function spaces with a strong inductive bias is a central challenge in soft computing, especially when training data are scarce and standard deep architecture…
Nonlocal operator learning for fMRI encoding and decoding tasks
Andreas Kramer, Saugat Acharya, Alice Giola +1
Functional MRI data exhibit high-dimensional spatiotemporal structure, making both prediction and decoding challenging. In this work, we investigate neural integral-operator-based…
Universal Approximation of Operators with Transformers and Neural Integral Operators
Emanuele Zappala, Maryam Bagherian
We study the universal approximation properties of transformers and neural integral operators for operators in Banach spaces. In particular, we show that the transformer architectu…
Leray-Schauder Mappings for Operator Learning
Emanuele Zappala
We present an algorithm for learning operators between Banach spaces, based on the use of Leray-Schauder mappings to learn a finite-dimensional approximation of compact subspaces.…
Non-Markovian Discrete Diffusion with Causal Language Models
Yangtian Zhang, Sizhuang He, Daniel Levine +7
Discrete diffusion models offer a flexible, controllable approach to structured sequence generation, yet they still lag behind causal language models in expressive power. A key lim…
CaLMFlow: Volterra Flow Matching using Causal Language Models
Sizhuang He, Daniel Levine, Ivan Vrkic +6
We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large la…